Workflow Automation Solutions

Welding has always been at the core of fabrication and manufacturing, yet it’s one of the most challenging processes to scale. Why? TIG welding, known for its precision and quality, is labor-intensive, time-consuming, and requires a high level of skill. But in today’s landscape, fabricators and manufacturers face growing pressure to deliver faster, at a lower cost, and with fewer skilled welders available. This is where welding automation is making an impact. Take TIG welding, for example. Traditional methods are limited by manual processes, but advancements like the SWR-TIPTIG system are showing how automation can improve both productivity and quality while addressing labor shortages. Here’s what’s changing: 1️⃣ Speed and precision coexist: Historically, TIG welding prioritized precision over speed. But with systems like TIPTIG, welders can achieve up to 2.6x faster deposition rates without sacrificing quality. 2️⃣ Expanding accessibility: Automation reduces the reliance on highly specialized welders, allowing a broader range of operators to achieve high-quality results. This isn’t about replacing welders—it’s about enabling them to work smarter. 3️⃣ Worker safety and ergonomics: Automation minimizes exposure to hazardous fumes, radiant heat, and repetitive physical strain. As a result, the operator is less fatigued and more focused on managing the process, not the physical task. 4️⃣ Reducing costs beyond the weld: Automation cuts down on post-weld cleanup with minimal spatter and increases overall throughput by integrating seamlessly into production workflows. These advancements are particularly critical in industries where weld integrity is non-negotiable, such as aerospace, oil & gas, and pharmaceuticals. Applications that involve exotic materials like stainless steel and Inconel demand precision that automation can now consistently deliver. The broader takeaway? Automation is no longer just a productivity tool—it’s a strategic decision for staying competitive. As project timelines tighten and customers demand higher quality at lower costs, adopting solutions that align with these expectations is essential. Those who embrace these innovations are not just improving processes—they’re shaping the future of fabrication. Let’s continue the conversation about where automation is taking us and how we can solve the challenges ahead. What do you see as the biggest hurdle to automation in your industry?

Still, many of us get confused about using LangChain or LlamaIndex. LangChain specializes in workflow orchestration, making it ideal for complex multi-step processes that chain together multiple LLM operations. It excels in applications requiring tool/API integrations, agent-based systems with reasoning capabilities, and scenarios needing extensive prompt engineering. LangChain also provides frameworks for evaluation and comparison of different approaches. LlamaIndex, on the other hand, focuses on document processing and data retrieval. Its strengths lie in handling complex document ingestion, advanced indexing of knowledge bases, and providing structured data access for LLMs. LlamaIndex is particularly valuable for customizing retrieval strategies, processing diverse document formats, and implementing query transformations and routing. When deciding between them, consider your primary focus: choose LangChain if your project involves complex workflows requiring multiple integrated steps and tools working together in sequence. Select LlamaIndex if your application centers on document processing, knowledge base creation, and sophisticated data retrieval strategies. You can in fact, if you want, can use both but that becomes a overhead and burden for your engineers. For many RAG projects, the choice depends on whether workflow orchestration or document processing capabilities are more critical to your specific implementation. Build Your First RAG Application Using LlamaIndex: https://lnkd.in/g6iN7dmz Here is my LangChain RAG tutorial for beginners: https://lnkd.in/gYYDdXwH Here is my video on creating powerful Agentic RAG applications using LlamaIndex: https://lnkd.in/gAUmmaju Here is my complete article on different LLM frameworks: https://lnkd.in/eZdxPGiR

𝗟𝗟𝗠 -> 𝗥𝗔𝗚 -> 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 -> 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 The visual guide explains how these four layers relate—not as competing technologies, but as an evolving intelligence architecture. Here’s a deeper look: 1. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) This is the foundation. Models like GPT, Claude, and Gemini are trained on vast corpora of text to perform a wide array of tasks: – Text generation – Instruction following – Chain-of-thought reasoning – Few-shot/zero-shot learning – Embedding and token generation However, LLMs are inherently limited to the knowledge encoded during training and struggle with grounding, real-time updates, or long-term memory. 2. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) RAG bridges the gap between static model knowledge and dynamic external information. By integrating techniques such as: – Vector search – Embedding-based similarity scoring – Document chunking – Hybrid retrieval (dense + sparse) – Source attribution – Context injection …RAG enhances the quality and factuality of responses. It enables models to “recall” information they were never trained on, and grounds answers in external sources—critical for enterprise-grade applications. 3. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 RAG is still a passive architecture—it retrieves and generates. AI Agents go a step further: they act. Agents perform tasks, execute code, call APIs, manage state, and iterate via feedback loops. They introduce key capabilities such as: – Planning and task decomposition – Execution pipelines – Long- and short-term memory integration – File access and API interaction – Use of frameworks like ReAct, LangChain Agents, AutoGen, and CrewAI This is where LLMs become active participants in workflows rather than just passive responders. 4. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 This is the most advanced layer—where we go beyond a single autonomous agent to multi-agent systems with role-specific behavior, memory sharing, and inter-agent communication. Core concepts include: – Multi-agent collaboration and task delegation – Modular role assignment and hierarchy – Goal-directed planning and lifecycle management – Protocols like MCP (Anthropic’s Model Context Protocol) and A2A (Google’s Agent-to-Agent) – Long-term memory synchronization and feedback-based evolution Agentic AI is what enables truly autonomous, adaptive, and collaborative intelligence across distributed systems. Whether you’re building enterprise copilots, AI-powered ETL systems, or autonomous task orchestration tools, knowing what each layer offers—and where it falls short—will determine whether your AI system scales or breaks. If you found this helpful, share it with your team or network. If there’s something important you think I missed, feel free to comment or message me—I’d be happy to include it in the next iteration.

You must know these 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 as an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. If you are building Agentic Systems in an Enterprise setting you will soon discover that the simplest workflow patterns work the best and bring the most business value. At the end of last year Anthropic did a great job summarising the top patterns for these workflows and they still hold strong. Let’s explore what they are and where each can be useful: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗖𝗵𝗮𝗶𝗻𝗶𝗻𝗴: This pattern decomposes a complex task and tries to solve it in manageable pieces by chaining them together. Output of one LLM call becomes an output to another. ✅ In most cases such decomposition results in higher accuracy with sacrifice for latency. ℹ️ In heavy production use cases Prompt Chaining would be combined with following patterns, a pattern replace an LLM Call node in Prompt Chaining pattern. 𝟮. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: In this pattern, the input is classified into multiple potential paths and the appropriate is taken. ✅ Useful when the workflow is complex and specific topology paths could be more efficiently solved by a specialized workflow. ℹ️ Example: Agentic Chatbot - should I answer the question with RAG or should I perform some actions that a user has prompted for? 𝟯. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Initial input is split into multiple queries to be passed to the LLM, then the answers are aggregated to produce the final answer. ✅ Useful when speed is important and multiple inputs can be processed in parallel without needing to wait for other outputs. Also, when additional accuracy is required. ℹ️ Example 1: Query rewrite in Agentic RAG to produce multiple different queries for majority voting. Improves accuracy. ℹ️ Example 2: Multiple items are extracted from an invoice, all of them can be processed further in parallel for better speed. 𝟰. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: An orchestrator LLM dynamically breaks down tasks and delegates to other LLMs or sub-workflows. ✅ Useful when the system is complex and there is no clear hardcoded topology path to achieve the final result. ℹ️ Example: Choice of datasets to be used in Agentic RAG. 𝟱. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: Generator LLM produces a result then Evaluator LLM evaluates it and provides feedback for further improvement if necessary. ✅ Useful for tasks that require continuous refinement. ℹ️ Example: Deep Research Agent workflow when refinement of a report paragraph via continuous web search is required. 𝗧𝗶𝗽𝘀: ❗️ Before going for full fledged Agents you should always try to solve a problem with simpler Workflows described in the article. What are the most complex workflows you have deployed to production? Let me know in the comments 👇 #LLM #AI #MachineLearning

Building LLM Agent Architectures on AWS - The Future of Scalable AI Workflows What if you could design AI agents that not only think but also collaborate, route tasks, and refine results automatically? That’s exactly what AWS’s LLM Agent Architecture enables. By combining Amazon Bedrock, AWS Lambda, and external APIs, developers can build intelligent, distributed agent systems that mirror human-like reasoning and decision-making. These are not just chatbots - they’re autonomous, orchestrated systems that handle workflows across industries, from customer service to logistics. Here’s a breakdown of the core patterns powering modern LLM agents : Breakdown: Key Patterns for AI Workflows on AWS 1. Prompt Chaining / Saga Pattern Each step’s output becomes the next input — enabling multi-step reasoning and transactional workflows like order handling, payments, and shipping. Think of it as a conversational assembly line. 2. Routing / Dynamic Dispatch Pattern Uses an intent router to direct queries to the right tool, model, or API. Just like a call center routing customers to the right department — but automated. 3. Parallelization / Scatter-Gather Pattern Agents perform tasks in parallel Lambda functions, then aggregate responses for efficiency and faster decisions. Multiple agents think together — one answer, many minds. 4. Saga / Orchestration Pattern Central orchestrator agents manage multiple collaborators, synchronizing tasks across APIs, data sources, and LLMs. Perfect for managing complex, multi-agent projects like report generation or dynamic workflows. 5. Evaluator / Reflect-Refine Loop Pattern Introduces a feedback mechanism where one agent evaluates another’s output for accuracy and consistency. Essential for building trustworthy, self-improving AI systems. AWS enables modular, event-driven, and autonomous AI architectures, where each pattern represents a step toward self-reliant, production-grade intelligence. From prompt chaining to reflective feedback loops, these blueprints are reshaping how enterprises deploy scalable LLM agents. #AIAgents

Machine Learning models are critical not only for many customer-facing products like recommendation algorithms but also very important for unlocking value in backend tasks to enable efficient operations and save business costs. This blog, written by the machine learning engineering team at Netflix, shares the team's approaches to automatically leverage machine learning to remediate failed jobs without human intervention. -- Problem: At Netflix, millions of workflow jobs run daily in their big data platform. Although failed jobs represent a small portion, they still incur significant costs given the large base. The team currently has a rule-based approach to categorize error messages. However, for memory configuration errors, engineers still need to remediate the jobs manually due to their intrinsic complexity. -- Solution: The team uses the existing rule-based classifier as the first pass to classify errors, and then develops a new Machine Learning service as the second pass to provide recommendations for memory configuration errors. This Machine Learning system has two components: one is a prediction model that jointly estimates the probability of retry success and the retry cost, and the other is an optimizer that recommends a Spark configuration to minimize the linear combination of retry failure probability and cost. -- Result: Such a solution demonstrated big success, with more than 56% of all memory configuration errors being remediated and successfully retried without human intervention. This also decreased the compute cost by about 50% because the new configurations make the retry successful or disable unnecessary retries. Whenever there is a place with inefficiencies, it's helpful to think about a better solution. Machine learning is the way to introduce intelligence into such solutions, and this blog serves as a nice reference for those interested in leveraging machine learning to improve their operational efficiency. #machinelearning #datascience #operation #efficiency #optimization #costsaving #snacksweeklyondatascience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gRcaifKR 

LLMOps in the Real World ⬇️ 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? There is plenty of advice on how to build agent prototypes that > use third-party API, like OpenAI or Antrhopic. > encapsulate all the agent + tooling logic inside a single Python program > run locally with docker compose But the thing is, companies out there need WAY MORE than this to extract actual business value from this technology. To scale this prototypes into fully working systems, without breaking the bank, you need to use the right infrastructure and tooling. This is what LLMOps in the Real World are. And this is what Marius Rugan and myself will start teaching from today. 𝗦𝘆𝘀𝘁𝗲𝗺 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 📐 Before we get our hands-dirty with specific tools and components, we need to understand the backbone and system architecture. Agentic workflows are way more than a Python program. They are a collection of services, running inside a Kubernetes cluster. (We we will cover "Why Kubernetes?" in our first video some time next week. Bear with me for a second here) These services are: > 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗳𝗸𝗹𝗼𝘄 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻𝘀, typically written in Python using a library like Langgraph or Crew AI, or even better in Rust. > 𝗟𝗟𝗠 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 running on GPU nodes, that serve the text completions the agent workflows need for reasoning, sumarization, tool parameter parsing... > 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 (MCP servers) and clients that connect agents to the internal services of your company (aka the tools), which can be - read-only, for example a data warehouse in PostgreSQL, or - read-and-write, for example the WhatsappAPI to send and receive customer messages. To understand the system is working the way you expect it to work, you need to collect and visualize logs and metrics, from all these services, using battle tested tooling like Prometheus Group, Grafana Labs and the new kid on the block Comet's Opik. 𝗔𝗴𝗲𝗻𝘁𝟮𝗔𝗴𝗲𝗻𝘁 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 🔮 As the number of agents increase, you can get extra value by enabling collaboration between them, using the newly released Agent2Agent protocol by Google. The future is exciting. Let's get there one step at a time. 𝗪𝗮𝗻𝗻𝗮 𝗹𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵? Enrol to one of my courses and get lifetime access to the Real World ML Community on Discord. Follow me so you don't miss what is coming next.

Agentic AI Design Patterns are emerging as the backbone of real-world, production-grade AI systems, and this is gold from Andrew Ng Most current LLM applications are linear: prompt → output. But real-world autonomy demands more. It requires agents that can reflect, adapt, plan, and collaborate, over extended tasks and in dynamic environments. That’s where the RTPM framework comes in. It's a design blueprint for building scalable agentic systems: ➡️ Reflection ➡️ Tool-Use ➡️ Planning ➡️ Multi-Agent Collaboration Let’s unpack each one from a systems engineering perspective: 🔁 1. Reflection This is the agent’s ability to perform self-evaluation after each action. It's not just post-hoc logging—it's part of the control loop. Agents ask: → Was the subtask successful? → Did the tool/API return the expected structure or value? → Is the plan still valid given current memory state? Techniques include: → Internal scoring functions → Critic models trained on trajectory outcomes → Reasoning chains that validate step outputs Without reflection, agents remain brittle, but with it, they become self-correcting systems. 🛠 2. Tool-Use LLMs alone can’t interface with the world. Tool-use enables agents to execute code, perform retrieval, query databases, call APIs, and trigger external workflows. Tool-use design involves: → Function calling or JSON schema execution (OpenAI, Fireworks AI, LangChain, etc.) → Grounding outputs into structured results (e.g., SQL, Python, REST) → Chaining results into subsequent reasoning steps This is how you move from "text generators" to capability-driven agents. 📊 3. Planning Planning is the core of long-horizon task execution. Agents must: → Decompose high-level goals into atomic steps → Sequence tasks based on constraints and dependencies → Update plans reactively when intermediate states deviate Design patterns here include: → Chain-of-thought with memory rehydration → Execution DAGs or LangGraph flows → Priority queues and re-entrant agents Planning separates short-term LLM chains from persistent agentic workflows. 🤖 4. Multi-Agent Collaboration As task complexity grows, specialization becomes essential. Multi-agent systems allow modularity, separation of concerns, and distributed execution. This involves: → Specialized agents: planner, retriever, executor, validator → Communication protocols: Model Context Protocol (MCP), A2A messaging → Shared context: via centralized memory, vector DBs, or message buses This mirrors multi-threaded systems in software—except now the "threads" are intelligent and autonomous. Agentic Design ≠ monolithic LLM chains. It’s about constructing layered systems with runtime feedback, external execution, memory-aware planning, and collaborative autonomy. Here is a deep-dive blog is you would like to learn more: https://lnkd.in/dKhi_n7M

Relying on one LLM provider like OpenAI is risky and often leads to unnecessary high costs and latency. But there's another critical challenge: ensuring LLM outputs align with specific guidelines and safety standards. What if you could address both issues with a single solution? This is the core promise behind Portkey's open-source AI Gateway. AI Gateway is an open-source package that seamlessly integrates with 200+ LLMs, including OpenAI, Google Gemini, Ollama, Mistral, and more. It not only solves the provider dependency problem but now also tackles the crucial need for effective guardrails by partnering with providers such as Patronus AI and Aporia. Key features: (1) Effortless load balancing across models and providers (2) Integrated guardrails for precise control over LLM behavior (3) Resilient fallbacks and automatic retries to guarantee your application recovers from failed LLM API requests (4) Adds minimal latency as a middleware (~10ms) (5) Supported SDKs include Python, Node.JS, Rust, and more One of the main hurdles to enterprise AI adoption is ensuring LLM inputs and outputs are safe and adhere to your company’s policies. This is why projects like Portkey are so useful. Integrating guardrails into an AI gateway creates a powerful combination that orchestrates LLM requests based on predefined guardrails, providing precise control over LLM outputs. Switching to more affordable yet performant models is a useful technique to reduce cost and latency for your app. I covered this and eleven more techniques in my last AI Tidbits Deep Dive https://lnkd.in/gucUZzYn GitHub repo https://lnkd.in/g8pjgT9R

I have spent much time recently discussing innovations in the food service sector, like mobile ordering, robotics & automation, and ghost kitchens. This innovation is being driven by customer demand for more convenient food options. Therefore, I was interested to read Heather Haddon’s article “Drones and ‘Game Film’: Inside Chick-fil-A’s Quest to Make Fast Food Faster” in the The Wall Street Journal. For years, Chick-fil-A Restaurants' popularity has resulted in long lines of cars, causing major congestion at some locations, frustrating customers and nearby residents, businesses and municipal leaders. Heather provided an overview of how Chick-fil-A is using data analytics and video analyses, like professional sports teams, dispatching specialist teams from its headquarters to its more than 3,000 restaurants to study the minutiae of parking-lot traffic patterns and how employees hand off orders. By integrating data from security cameras in the kitchen and drones outside the restaurant, Chick-fil-A was able to see that more workers were needed to reduce the burden on existing employees working the drive-through and the Wi-Fi used by parking-lot order-takers needed to be extended further from the store. By using visual data, Chick-fil-A was able to identify bottlenecks, as well as test and analyze different solutions, enabling the company to be at the forefront of fast-food drive-through science, and adjust to changing consumer patterns. One of the biggest takeaways from this work is that Chick-fil-A realized it had underestimated how many different challenges it faced. Chick-fil-A is the same company that in 2024 opened a multi-story, drive-through only restaurant in Georgia. This new restaurant design can handle three times as many drive-through cars as its other restaurants and includes lanes just for customers who order through the chain's app. The kitchen is two times larger than a typical Chick-fil-A restaurant kitchen and utilizes a food conveyor system to deliver a meal every six seconds, according to Chick-fil-A. This food conveyor system is an example of how the use of automation & robotics is changing the FoodTech sector. With an almost unlimited amount of data available from security cameras, sensors and other devices throughout the facilities and the growing power of AI and machine learning (ML), we should expect that other quick service restaurants will follow a similar strategy to optimize operations, to reduce costs and improve the consumer experience. https://lnkd.in/gsF6YeyH #ai; #robotics; #automation; #innovation; #technology; #restaurants; #foodtech; #food EcoTech Capital Cy Obert